Column

Discrimination

Item ratings

User Ratings

Column

Method

Simulations

Simulate responses and response times based on known \(\theta\)’s, \(\beta\)’s and \(a\)-parameters. Look how wel we can recover the \(a\)-parameter.

  • Simulate full data
    • Estimate with LTM
    • GLM
    • Newton-Rapson
  • Simulate sparse data
    • LTM
    • GLM
    • Newton-Rapson

Look at BIAS and SEM.

Proliminary Results

To-DO

  • \(a = 1\)
  • \(a \sim U(0,3)\)
  • \(a \sim U(-.5,3)\)
  • Apply NR in Math Garden
  • Inactivate bad items
  • Apply to real data
---
title: "Item Discrimination in Math Garden"
author: "Sharon Klinkenberg"
output: 
  flexdashboard::flex_dashboard:
    logo: http://shklinkenberg.github.io//statistics-lectures/template/logo_uva.png
    orientation: col
    social: menu
    source_code: embed
    vertical_layout: fill
    navbar:
      - { icon: "ion-android-contact", href: "http://www.uva.nl/en/profile/k/l/s.klinkenberg/s.klinkenberg.html", align: right }
      - { icon: "fa-download", href: "poster.pdf", align: right }
---

```{r setup, include=FALSE}
library(flexdashboard)
```

Inputs {.sidebar}
-----------------------------------------------------------------------

Research

The aim of this study is to find a feasable method to determine item discrimination values within the Math Garden to detect deviant items. __What is the problem__ * Sparse data * Scaling * Identifiability __Pragmatic solution__ So there are some fundamental problems in estimating a. But can we at least have some pragmatic solution? __For fancy visuals see QR code__
https://goo.gl/a6ch6A
Column {data-width=500} ----------------------------------------------------------------------- ### Discrimination ![](Proportion_correct.gif) ### Item ratings ![](item_distributions.gif) ### User Ratings ![](user_distributions.gif) Column ----------------------------------------------------------------------- ### Method __Simulations__ Simulate responses and response times based on known $\theta$'s, $\beta$'s and $a$-parameters. Look how wel we can recover the $a$-parameter.
* Simulate full data * Estimate with LTM * GLM * Newton-Rapson * Simulate sparse data * LTM * GLM * Newton-Rapson
Look at BIAS and SEM. ### Proliminary Results ![](scatterplots_for_all_estimation_methods.jpg) ### To-DO {data-height=200}
* $a = 1$ * $a \sim U(0,3)$ * $a \sim U(-.5,3)$ * Apply NR in Math Garden * Inactivate bad items * Apply to real data